Requirements For Real-Time Clinical Decision Support Drools Workshop 2010 San Diego, CA
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Transcript of Requirements For Real-Time Clinical Decision Support Drools Workshop 2010 San Diego, CA
Requirements For Real-Time Clinical Decision Support
Drools Workshop 2010San Diego, CA
Emory Fry, MD23 March 2010
Background
• Clinical Decision Support a key to realizing “Meaningful Use”, personalized healthcare, patient safety and self-empowerment
• DDSS/KMR is a DoD effort to create a reference implementation of a real-time CDS service
• Leverages our work creating a standards-based, semantically constrained SOA for the Nationwide Health Information Network
www.SocraticGrid.org
Changing Behavior: Healthcare's “Last Mile”
• Achieving & Sustaining Health • Knowing what to do• Knowing how to do it• Knowing when to do it
• Behavioral Entropy• Workflow reengineering• Making it easier to do the right thing than not• Achieving the highest degree of healthy behavior with a state
of stable energy expenditure
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Behavioral Entropy
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Functional Scenarios
• Scenarios and Use Cases at www.SocraticGrid.org
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Mobility Applications
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Weight Reduction Plan Nonlinear – Dr. Thomas Patient: Doe, John A.
Wei
ght
in lb
s.
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0/20
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1/31
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213211
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Plan Evaluation Dates/Milestones vs. Actuals
247
Begin End
Actuals
Planned
Personalized Care Plan
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Encode the Logic of Care
• Context: When is a specific decision / rule appropriate?
• Data: What information is required to make this decision?
• Decision Rule: What logical expressions encode this decision?
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Encode the Workflow of Care
• How is the decision process triggered?• How are decisions communicated to the right
user at the right time and place?• Does the system help accomplished the
suggested tasks?• How are alerts/suggestions acknowledged by the
user?
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ModelingEnvironmentsfor Developing
Logic
ExchangeLanguage For
Logic / Workflow
SystemStrategy for
Implementation
Flowcharts/Editors?Flowcharts/Editors?
Knowledge RepositoryCDS System
HL7
DECISION REQ MSG
Email Pager ???
EventService
Fact Queue
Fact Handler
Decision Request Queue
TaskService
Decision Support Service
Integration Engine
HIS
Source
Decision Support Service & Knowledge Management Repository
HL7HL7
Metadata
Private Rules
Institutional Rules
Notification Rules
KMR Service
CAL
Architectural Design
DDSS-KMR Approach
• Develop data models using NHIN standards, HL7 CDS Working Group, IHE, and NIEM
• Identify standardized terminologies when ever possible for value sets (i.e., LOINC, SNOMED, etc)
• Create “placeholder” value sets for concepts without exiting terminologies to support the use cases
• Working with OASIS / HL7 on security framework for access control to clinical objects or “facts”
• Working with HL7, Morningside, Partners, and others on developing functional semantics for workflow actions
www.SocraticGrid.org
Email Pager System Message
EventService
TaskService
Integration Engine
HIS
Source
Decision Support Service: Rules Individualized To Patient
HL7HL7
Metadata
Private Rules
Institutional Rules
Notification Rules
KMR Service
CAL
Decision Support Service
Working Memory Patient #1
Decision Support Service
Working Memory Patient #2
Decision Support Service
Working Memory Patient #3
Order
New Fact
Decision Support Service Patient #1
EventService
Integration Engine
HIS
Source
Working Memory & The Virtual Medical Record
HL7HL7
CAL
Dx Fact Rx Fact Lab FactDemographic Fact
Dx Fact Rx Fact Lab FactDemographic Fact
Dx Fact Rx Fact Lab FactDemographic Fact
Dx Fact Rx Fact Lab FactDemographic Fact
Virtual Medical Record
Drools Rule Engine
New Requirements For Drools
• Loading and unloading of rules / knowledge bases• Rule engine state management• Scalability• Inference Engine performance• Rich, fully functional Web Service API• Remote invocation• Workflow functionality and human task
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Questions?
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Limitations
• Current Medical Vocabularies / Ontologies Inadequate• No organizational ontology• No functional ontology• No operational environment terminologies
• Data Structures Inadequately Constrained• Security Framework • Medical-Legal Framework
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AIM #1
• Fully document KMR and Clinical Decision Support Engine functional requirements, technical design, and interface controls using MHS standard documentation methodologies
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AIM #2
• Develop the technical infrastructure and tools to support domain knowledge development, management, dissemination, and run-time execution of computable clinical decision support algorithms and automated clinical practice guidelines as a service for the NHIN bus
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AIM #3
• Demonstrate execution of these computable clinical guidelines using the MHS AHLTA client and data repository
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